Discriminating urban vegetation from a metropolitan matrix through partial unmixing with hyperspectral AVIRIS data

In the semi-arid western United States, urbanization transforms landscapes from sparsely vegetated grasslands into matrices of asphalt, concrete, turf grass, and multistrata wooded stands. Such land-cover change affects ecological processes, such as carbon (C) storage. This research investigates quantifying the vegetated and anthropogenic components of urbanized landscapes, in order to contribute ultimately to further ecological process studies, through a case study of Boulder, Colorado. Rather than map the urban land-cover types, the primary motivation was to extract biophysical information from hyperspectral imagery to understand how urbanization shifts regional biomass. Using convex geometry and partial unmixing algorithms with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery, major landscape elements were identified, including five vegetation endmembers that comprised cultivated and natural vegetation (both herbaceous and woody), soil, water, and five impervious surfaces. Urban vegetation equalled or exceeded surrounding vegetation fractional abundance. Trees were detected in the city center, and trees and grass intermingled in neighborhoods. Thus, this study expands the multispectral unmixing of the vegetation impervious surface soil model and demonstrates a viable method for mapping the composition of urban areas. Shifts in vegetation due to urbanization are best detected through biophysical remote sensing of actual ground components.